A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems

This study described a hybrid model for supporting the personnel selection process in manufacturing systems. Personnel selection is a very important issue for an effective manufacturing system, since the improper personnel might cause many problems affecting productivity, precision, flexibility and quality of the products negatively. On the other hand, selecting the best personnel among many alternatives is a multi-criteria decision making (MCDM) problem. In this study, a hybrid model which employs analytic network process (ANP) and modified TOPSIS (Technique for Order Performance by Similarity to Idea Solution) together, is proposed for the personnel selection problem. The ANP is used to analyze the structure of the personnel selection problem and to determine weights of the criteria, and modified TOPSIS method is used to obtain final ranking. To illustrate how the approach is used for the personnel selection problem, an application of a real case in a company is conducted. The application has demonstrated the effectiveness and feasibility of the proposed model. Company management found the application and results satisfactory and implementable in their personnel selection process.     

An improved TOPSIS approach for the competitiveness analysis of provincial information resource industries in China

The development of information resources industries is becoming increasingly important for improving the competitiveness of individual economies. As a result, effectively evaluating the performance of individual information resources industries becomes a critical problem that needs to be adequately addressed. This paper presents an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach for evaluating the performance of provincial information resources industries in an objective manner in China. The concept of information entropy is appropriately used for determining the objective weighting of the evaluation criteria in the evaluation process. The Mahalanobis distance is adequately adopted for better reflecting the relative distance between individual alternatives and the idea solution in determining the overall performance of individual alternatives across all the evaluation criteria. This leads to the determination of consistent performance evaluation outcomes, which are better accepted by all the stakeholders in the evaluation process. An example of evaluating the competitiveness of provincial information resources industries in China is presented. The result shows that the improved TOPSIS approach is effective for evaluating the competitiveness of information resources industries in the real‐world setting.     

Evaluation and management of new service concepts: An ANP-based portfolio approach

One of the most crucial decisions in service development is concept selection. Nevertheless, little attention has been paid to evaluation of new service concepts (NSCs). This study proposes an analytic network process (ANP) approach to evaluation of NSCs. ANP is a multiple criteria decision making (MCDM) method that can accommodate interdependency among decision attributes. The proposed approach measures feasibility of NSCs in terms of strategy, technology, market, implementation, and operation. The derived feasibility values of NSC alternatives are then employed to construct the NSC portfolio matrix, together with customers’ preference. The NSC portfolio matrix is expected to aid decision making on concept selection and provide managerial implications for service development. A case of the mobile information and entertainment service is presented to illustrate the proposed approach.     

Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection

In the present paper, a novel fuzzy Multiple Attribute Decision Making (MADM) model is proposed for modeling and solving truck selection problem of a land transportation company. As truck selection is a very crucial problem for the land transportation companies, a systematic and scientific approach is necessary for its solution. A systematic methodology is proposed in the present study by integrating “fuzzy DEMATEL” and “fuzzy hierarchical TOPSIS”. The proposed method makes use of fuzzy DEMATEL method for evaluating the weights of the criteria and hierarchical fuzzy TOPSIS method for assessing the alternatives according to criteria. The steps of the method are described first then a case study is presented in the paper.     

Evaluation of freight villages: A GIS-based multi-criteria decision analysis

The development of freight villages in a given region is often viewed as a popular project in the development of a particular country's logistic plans. However, the evaluation of a freight village location among the various alternatives is a multi-criteria decision making (MCDM) problem including both quantitative and qualitative criteria. Obtaining relevant geographic data is another problem for the evaluation of freight village locations. In this study, a geographic information system (GIS)-based MCDM model is proposed to evaluate potential locations for freight villages. The proposed model consists of four sequential phases. In the first phase, different geographic criteria are determined from existing literature, and data are gathered using GIS. By criteria weights equally, alternative locations are determined through GIS in the second phase. In the third phase, identified criteria are weighted using analytical network process (ANP) method by various stakeholders of potential freight villages. In the fourth phase, the “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)” method is applied in order to determine the best alternative based on the weighted criteria. Using real data from the city of Ankara, Turkey, we test the applicability of our evaluation approach and compare the best alternative obtained by the proposed method with the current freight village (CFV) location in Ankara.     

A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard problem since supplier selection is typically a multi criteria group decision-making problem involving several conflicting criteria on which decision maker’s knowledge is usually vague and imprecise. In this study, TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate supplier in group decision making environment. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives. Finally, a numerical example for supplier selection is given to illustrate application of intuitionistic fuzzy TOPSIS method.     

Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory

Design concept evaluation is a critical stage in the product development which has significant impact on the downstream process in product development thus on success of new product. Design concept evaluation is widely recognized as a complex multi-criteria decision-making (MCDM) problem involving various decision criteria and large amount of data which are usually imprecise and subjective. This paper proposes a new decision-making method to evaluate product design concepts based on the distance between interval vectors each alternative and positive and negative ideal reference vectors. Rank of design concepts is obtained by calculating interval-based relative closeness index for each alternative. In this method, to deal with uncertainty and vagueness of data in the primary phases of product design, performance of design concepts with respect to quantitative and qualitative criteria are concurrently evaluated using rough set and fuzzy set. The weights of criteria used in the evaluation are obtained using the extent analysis method on fuzzy AHP. The efficacy of the method is demonstrated with a numerical example and the results are compared to TOPSIS method. In final, the conclusions of our method are represented and some future directions are proposed to improve the model.     

An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies

The selection of Computer-Integrated Manufacturing (CIM) technologies becomes more complex as the decision makers in the manufacturing organization have to assess a wide range of alternatives based on a set of attributes. Although, a lot of Multi-Attribute Decision-Making (MADM) methods are available to deal with selection applications, this paper aims to explore the applicability of an integrated TOPSIS and DoE method to solve different CIM selection problems in real-time industrial applications. Four CIM selection problems, which include selection of (a) an industrial robot, (b) a rapid prototyping process, (c) a CNC machine tool and (d) plant layout design, are considered in this paper. TOPSIS method and Design of Experiment (DoE) are used together to identify critical selection attributes and their interactions of all these cases by fitting a polynomial to the experimental data in a multiple linear regression analysis. This mathematical model development process involves TOPSIS experiments with the model. The regression meta-model greatly reduced the cost, time and amount of the calculation step in application the TOPSIS model. Application results were validated and shown that they provide good approximations to four decision making problem's results in the literature.     

A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection

Supplier selection has become a very critical activity to the performance of organizations and supply chains. Studies presented in the literature propose the use of the methods Fuzzy TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy AHP (Fuzzy Analytic Hierarchy Process) to aid the supplier selection decision process. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of these two methods in the context of supplier selection decision making. The comparison was made based on the factors: adequacy to changes of alternatives or criteria; agility in the decision process; computational complexity; adequacy to support group decision making; the number of alternative suppliers and criteria; and modeling of uncertainty. As an illustrative example, both methods were applied to the selection of suppliers of a company in the automotive production chain. In addition, computational tests were performed considering several scenarios of supplier selection. The results have shown that both methods are suitable for the problem of supplier selection, particularly to supporting group decision making and modeling of uncertainty. However, the comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers. Thus, this comparative study contributes to helping researchers and practitioners to choose more effective approaches for supplier selection. Suggestions of further work are also proposed so as to make these methods more adequate to the problem of supplier selection.     

An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management

Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, this study proposes integrated fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multi-choice goal programming (MCGP) approach to solve the supplier selection problem. The advantage of this method is that it allows decision makers to set multiple aspiration levels for supplier selection problems. The integrated model is illustrated by an example in a watch firm.     

The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making

The technique for order preference by similarity to ideal solution (TOPSIS) method is a well-known compromising method for multiple criteria decision analysis. This paper develops an extended TOPSIS method with an inclusion comparison approach for addressing multiple criteria group decision-making problems in the framework of interval-valued intuitionistic fuzzy sets. Considering the relative agreement degrees and the importance weights of multiple decision makers, this paper presents a modified hybrid averaging method with an inclusion-based ordered weighted averaging operation for forming a collective decision environment. Based on the main structure of the TOPSIS method, this paper utilizes the concept of inclusion comparison possibilities to propose a new index for an inclusion-based closeness coefficient for ranking the alternatives. Additionally, two optimization models are established to determine the criterion weights for addressing situations in which the preference information is completely unknown or incompletely known. Finally, the feasibility and effectiveness of the proposed methods are illustrated by a medical group decision-making problem.     

A systematic approach for developing a new business model using morphological analysis and integrated fuzzy approach

Despite the growing importance of developing a novel business model, most previous studies on business model remain conceptual and theoretical. A paucity of empirical studies has hindered the use of the business model concept in practical purposes. Hence, this study proposes a systematic approach to new business model development (NBMD) that helps business practitioners to develop, evaluate and select the best business model to meet the business objectives. The proposed approach comprises two stages: identification of business model alternatives and business model evaluation and selection. During the first stage, a set of business model alternatives are derived by exploring all the possible combinations of a morphological matrix, and in the second stage, we conduct an evaluation and selection of a suitable business model. Morphological analysis (MA) has been employed for the derivation and aggregation of business model alternatives, and decision-making approach that integrates fuzzy extent analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods is used as an evaluation and selection tool. Finally, to illustrate the applicability of proposed approach, a case study on the development of a business model for a Telco has been presented.     

On the effect of subjective,objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites

To date, no specific framework has been developed to guide composite structure designers to select the optimum fiber types and fabric weave patterns for a given application. This article aims to, first, investigate the effect of weighting methods in multiple criteria decision making (MCDM) and then arrive at a systematic framework for optimum weave pattern selection in fiber reinforced polymer (FRP) composites. Namely, via measured data from an industrial case study, the TOPSIS MCDM technique has been applied to choose the best candidate among different polypropylene/glass laminates. As an input to TOPSIS, different types of subjective and objective weighting methods were initially compared to assess the role of relative importance values (weights) of design criteria. These included the Entropy method, the modified digital logic (MDL) method, and the criteria importance through inter-criteria correlation (CRITIC) method. Next, two new subjective weighting methods, named ‘Numeric Logic (NL)’ and ‘Adjustable Mean Bars (AMB)’ methods, were introduced to give more practical and effective means to the decision makers during the weighting of criteria. In particular, compared to the MDL, the NL method increased the accuracy of assigned weights for an expert DM. On the other hand, the AMB provided a more interactive, visual approach through MCDM weighting process for less experienced DMs. Finally, a generalized combinative weighting framework is presented to show how different types of weightings may be combined to find more reliable rankings of alternatives. The combinative weighting could specifically accommodate different scenarios where a group of designers are involved and have different levels of experience, while given a large number of alternatives/criteria in highly nonlinear applications such as impact design of composite materials.     

Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem

Supplier selection is nowadays one of the critical topics in supply chain management. This paper presents a new decision making approach for group multi-criteria supplier selection problem, which clubs supplier selection process with order allocation for dynamic supply chains to cope market variations. More specifically, the developed approach imitates the knowledge acquisition and manipulation in a manner similar to the decision makers who have gathered considerable knowledge and expertise in procurement domain. Nevertheless, under many conditions, exact data are inadequate to model real-life situation and fuzzy logic can be incorporated to handle the vagueness of the decision makers. As per this concept, fuzzy-AHP method is used first for supplier selection through four classes (CLASS I: Performance strategy, CLASS II: Quality of service, CLASS III: Innovation and CLASS IV: Risk), which are qualitatively meaningful. Thereafter, using simulation based fuzzy TOPSIS technique, the criteria application is quantitatively evaluated for order allocation among the selected suppliers. As a result, the approach generates decision-making knowledge, and thereafter, the developed combination of rules order allocation can easily be interpreted, adopted and at the same time if necessary, modified by decision makers. To demonstrate the applicability of the proposed approach, an illustrative example is presented and the results analyzed.     

Developing a fuzzy TOPSIS approach based on subjective weights and objective weights

Multiple criteria decision making (MCDM) is widely used in ranking one or more alternatives from a set of available alternatives with respect to multiple criteria. Inspired by MCDM to systematically evaluate alternatives under various criteria, we propose a new fuzzy TOPSIS for evaluating alternatives by integrating using subjective and objective weights. Most MCDM approaches consider only decision maker’s subjective weights. However, the end-user attitude can be a key factor. We propose a novel approach that involves end-user into the whole decision making process. In this proposed approach, the subjective weights assigned by decision makers (DM) are normalized into a comparable scale. In addition, we also adopt end-user ratings as an objective weight based on Shannon’s entropy theory. A closeness coefficient is defined to determine the ranking order of alternatives by calculating the distances to both ideal and negative-ideal solutions. A case study is performed showing how the propose method can be used for a software outsourcing problem. With our method, we provide decision makers more information to make more subtle decisions.     

Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process

The basic concept in axiomatic design (AD) is the existence of design axioms. First of these axioms is the independence axiom and the second one is the information axiom. Information axiom proposes the selection of the best alternative that has minimum information. Analytic hierarchy process (AHP) is another multi-attribute method which is a decision-making method for selecting the best among a set of alternatives, given some criteria. The method has been extensively applied, especially in large-scale problems where many criteria must be considered and where the evaluation of alternatives is mostly subjective. Multi-attribute transportation company selection is a very important activity for effective supply chain. Selection of the best company under determined criteria (such as cost, time, damage/loss, flexibility and documentation ability) using both multi-attribute AD and AHP will be realized in this study. The fuzzy multi-attribute AD approach is also developed and it is compared by one of fuzzy AHP methods in the literature. The selection process has been accomplished by aiding a software that includes crisp AD and fuzzy AD.     

An ERP model for supplier selection in electronics industry

Asus Tech. is the largest motherboard manufacturer in Taiwan. Hundreds of suppliers cooperate with the company in business. So that supplier selection is the most important function of a purchasing department in the enterprise. An enterprise resource planning (ERP) system in the process of supplier selection may result in the great savings in both costs and man hours. In the concept of push and pull, an ERP system acts as an efficient tool in the resource integration and profit creation for a company. Through ERP, a decision manager can clearly realize the strength and weakness of the purchasing operation. To establish a real-time purchasing environment, a methodology of analytic network process (ANP), technique for order preference by similarity to ideal solution (TOPSIS) and linear programming (LP) are effectively applied in the supplier selection process. ANP and TOPSIS are used to calculate the weight and give suppliers a ranking; LP effectively allocates order quantity to each vendor. As to the result, four PC board suppliers are given orders for 1200, 727, 1000 and 73 pieces.     

A multi-objective programming approach,integrated into the TOPSIS method,in order to optimize product design; in three-dimensional concurrent engineering

This paper proposes a new method to address a three-dimensional concurrent engineering (3D-CE) approach. It uses a Multi-Objective Linear Programming (MOLP) model integrated to the TOPSIS method in order to determine the best configuration product design, assembly process and suppliers of components. This method is able to involve various supply chain strategies by considering qualitative and quantitative criteria in the early stages of the new product development (NPD) process. It also applies the Fuzzy Analytic Hierarchy Process (FAHP) method to determine the relative importance of criteria defined to evaluate configurations of design alternatives, assembly processes and suppliers of components. A sensitivity analysis from different parameters in the model that affect the selection of the best design alternative is performed by creating a computer program using visual studio. A numerical example is used to demonstrate method efficacy and finally, the paper suggests some areas for future research.     

A decision support system for material and manufacturing process selection

The material and manufacturing process selection problem is a multi-attribute decision-making problem. These decisions are made during the preliminary design stages in an environment characterized by imprecise and uncertain requirements, parameters, and relationships. Material and process selection decisions must occur before design for manufacturing can begin. This paper describes a prototype material and manufacturing process selection system called MAMPS that integrates a formal multi-attribute decision model with a relational database. The decision model enables the representation of the designer's preferences over the decision factors. A compatibility rating between the product profile requirements and the alternatives stored in the database for each decision criteria is generated using possibility theory. The vector of compatibility ratings are aggregated into a single rating of that alternative's compatibility. A ranked set of compatible material and manufacturing process alternatives is output by the system. This approach has advantages over existing systems that either do not have a decision module or are not integrated with a database.     

Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage.